We compare our method to several state-of-the-art systems in both automatic and human evaluations. Our model achieves overall better performance measured by SARI [1]} and other metrics, showing that the generated outputs are more similar to those written by human editors. We also demonstrate that our model can control the extent of each simplification operation by: (1) imposing a soft constraint on the percentage of words to be copied from the input in the seq2seq model, thus limiting lexical paraphrasing; and (2) selecting candidates that underwent a desired amount of splitting and/or deletion. Finally, we create a new test dataset with multiple human references for Newsela [2]}, the widely used text simplification corpus, to specifically evaluate lexical paraphrasing.
